Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach

• Published in: Expert systems with applications Vol. 140; p. 112896
• Main Authors: Bandara, Kasun, Bergmeir, Christoph, Smyl, Slawek
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.02.2020; Elsevier BV
• Subjects: Algorithms; Big data forecasting; Clustering; Competition; Datasets; Domains; Forecasting; Forecasting techniques; LSTM; Mathematical models; Neural networks; Recurrent neural networks; RNN; Subgroups; Time series; Time series clustering; Big data forecasting; RNN; LSTM; Time series clustering; Neural networks

•Building a model on a set of related time series can improve the forecast accuracy.•Performance of the global models can degenerate if built on disparate time series.•A subgrouping strategy then augments the accuracies of the baseline global models. With the advent of Big Data, nowadays in many app...